ACTG 315 Short-Term Viral Dynamic Data for NLME Model Fitting

1. The data were produced from AIDS Clinical Trials Group, ACTG 315 study, which is sponsored by NIAID/NIH. If you have any questions regarding the data, you may contact Dr. Hulin Wu by email, hwu@bst.rochester.edu.

2. The detailed biomedical findings from this study and more detailed description of the study design can be found from the primary and secondary publications of this study, Lederman et al (1998) and Connick et al. (2000) (see below for these references).

3. Some early viral dynamic modeling analyses based on this study are reported in the following papers:

Wu and Ding and DeGruttola (1998, Statistics in Medicine)

Wu, Kuritzkes and McClernon, et al (1999, J of Infect Dis)

Wu and Ding (1999, Biometrics)

Ding and Wu (1999, Mathematical Biosciences)

Ding and Wu (2000, Biometrics)

Some further viral dynamic analyses for this study (including long-term viral dynamics and covariates analyses) can be found in other pages of our website.

4. This data set was slightly modified from the data used in Wu and Ding (1999, Biometrics). More data were added in the database. The data were re-cleaned after the analysis of Wu and Ding (1999). Only first 12-week data are included. The rebounded viral load data are deleted based on the description in Wu and Ding (1999).

5. Among total 53 patients accrued in this study, five patients dropped out before Week 12 due to intolerance and other problems. They are excluded from our analysis. Negative time (days) is the pre-entry measurements before antiviral treatment initiated.

6. Detection limit of the viral load (HIV RNA copies) assay is 100 copies per ml blood. If it is below detection, we imputed 100 in the data set although we imputed as 50 in our analysis (Wu and Ding 1999). If more than one measurement below detectable level for an individual, we just impute the first measurement and exclude the rest of them in our analysis (otherwise it may result in misleading dynamic patterns).

7. Fit the updated data using Wu and Ding (1999) method:

Delete the data at baseline (Day 0) and before treatment.

Other methods to deal with baseline data can be found in Ding and Wu (2000).

Due to convergence problems, multiple starting values (or global search algorithms) should be used to fit the NLME models. The starting point in the following Splus code was chosen after comparing the results using many different starting values.

SPLUS CODE:
######################### Beginning of Splus Code ########################
# Input Data: assume that the data at the end is stored in an ASCII file
# named "data".
workd<-read.table(file="data",header=T,row.names=NULL)

# The measurements before treatment initiation are not used in the analysis.
workd<-workd[workd$Day>0,]